Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability

Satellite-derived vegetation phenophases are frequently used to study the response of ecosystems to climate change. However, limited studies have identified the common phenological variability across different climate and vegetation zones. Using NOAA/Advanced Very High Resolution Radiometer (AVHRR)...

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Main Authors: Quansheng Ge, Junhu Dai, Huijuan Cui, Huanjiong Wang
Format: Article
Language:English
Published: MDPI AG 2016-05-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/8/5/433
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spelling doaj-a8211b3d5d354ead9934e6dbc606fd832020-11-24T22:40:53ZengMDPI AGRemote Sensing2072-42922016-05-018543310.3390/rs8050433rs8050433Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate VariabilityQuansheng Ge0Junhu Dai1Huijuan Cui2Huanjiong Wang3Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, ChinaSatellite-derived vegetation phenophases are frequently used to study the response of ecosystems to climate change. However, limited studies have identified the common phenological variability across different climate and vegetation zones. Using NOAA/Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset, we estimated start of growing season (SOS) and end of growing season (EOS) for Chinese vegetation during the period 1982–2012 based on the Midpoint method. Subsequently, the empirical orthogonal function (EOF) analysis was applied to extract the main patterns of phenophases and their annual variability. The impact of climate parameters such as temperature and precipitation on phenophases was investigated using canonical correlation analysis (CCA). The first EOF mode of phenophases exhibited widespread earlier or later SOS and EOS signals for almost the whole country. The attendant time coefficients revealed an earlier SOS between 1996 and 2008, but a later SOS in 1982–1995 and 2009–2012. Regarding EOS, it was clearly happening later in recent years, mainly after 1993. The preseason temperature contributed to such spatiotemporal phenological change significantly. The first pair of CCA patterns for phenology and preseason temperature was found to be similar and its time coefficients were highly correlated to each other (correlation coefficient >0.7). These results indicate that there is a substantial amount of common variance in SOS and EOS across different vegetation types that is related to large-scale modes of climate variability.http://www.mdpi.com/2072-4292/8/5/433remote sensing phenologygrowing seasonNDVIcanonical correlation analysis
collection DOAJ
language English
format Article
sources DOAJ
author Quansheng Ge
Junhu Dai
Huijuan Cui
Huanjiong Wang
spellingShingle Quansheng Ge
Junhu Dai
Huijuan Cui
Huanjiong Wang
Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability
Remote Sensing
remote sensing phenology
growing season
NDVI
canonical correlation analysis
author_facet Quansheng Ge
Junhu Dai
Huijuan Cui
Huanjiong Wang
author_sort Quansheng Ge
title Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability
title_short Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability
title_full Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability
title_fullStr Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability
title_full_unstemmed Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability
title_sort spatiotemporal variability in start and end of growing season in china related to climate variability
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2016-05-01
description Satellite-derived vegetation phenophases are frequently used to study the response of ecosystems to climate change. However, limited studies have identified the common phenological variability across different climate and vegetation zones. Using NOAA/Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset, we estimated start of growing season (SOS) and end of growing season (EOS) for Chinese vegetation during the period 1982–2012 based on the Midpoint method. Subsequently, the empirical orthogonal function (EOF) analysis was applied to extract the main patterns of phenophases and their annual variability. The impact of climate parameters such as temperature and precipitation on phenophases was investigated using canonical correlation analysis (CCA). The first EOF mode of phenophases exhibited widespread earlier or later SOS and EOS signals for almost the whole country. The attendant time coefficients revealed an earlier SOS between 1996 and 2008, but a later SOS in 1982–1995 and 2009–2012. Regarding EOS, it was clearly happening later in recent years, mainly after 1993. The preseason temperature contributed to such spatiotemporal phenological change significantly. The first pair of CCA patterns for phenology and preseason temperature was found to be similar and its time coefficients were highly correlated to each other (correlation coefficient >0.7). These results indicate that there is a substantial amount of common variance in SOS and EOS across different vegetation types that is related to large-scale modes of climate variability.
topic remote sensing phenology
growing season
NDVI
canonical correlation analysis
url http://www.mdpi.com/2072-4292/8/5/433
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